In contrast, our proposed solution (i) applies partial client participation for both computation and communication, (ii) updates the global model at each round, and (iii) leverages tracking in the global model update rule (which accounts for accelerated convergence with smoother paths). The server in FedADMM communicates with a fraction of clients during each round and clients only perform local training when selected. We establish convergence without restrictions on statistical heterogeneity among the networks, or the fraction of participating users. This is in contradistinction to the analysis of other baseline methods [1]}, [2]}, [3]} which requires assumptions on data dissimilarity that may be unrealistic in practices when data are individually generated by each client [4]}, [5]}, [6]}. Our proposed method has close connections to randomized ADMM [7]}, which in the FL setting amounts to randomly activating clients and the server. However, it requires that subproblems to be solved exactly, which may be impractical in consideration of heterogeneous hardware conditions and data volumes. On the other hand, FedADMM accounts for inexact solutions from local problems and allows variable amount of training performed at clients (quantified by a tunable parameter). Another related line of work is termed asynchronous ADMM [8]}, [9]}, [10]}, whose focus is on alleviating the straggler problem mentioned earlier. However, assumptions imposed therein such as the bounded delay assumption (i.e., each user needs to be active at least once every some number of rounds) may never be satisfied in FL settings.
<FIGURE>